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We present Sequence Salience, a visual tool for interactive prompt debugging with input salience methods.
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The cost structure of sensemaking
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Visgets: Coordinated visualizations for web-based information exploration and discovery
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The struggles of feature-based explanations: Shapley values vs. minimal sufficient subsets
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Captum: A unified and generic model interpretability library for pytorch
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Extraction of salient sentences from labelled documents
Misha Denil, Alban Demiraj, and Nando De Freitas. 2014 · 2014
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“Why should I trust you?”: Explaining the predictions of any classifier
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Towards a rigorous science of interpretable machine learning
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A unified approach to interpreting model predictions
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Axiomatic attribution for deep networks
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HotFlip: White-box adversarial examples for text classification
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SentencePiece: A simple and language independent subword tokenizer and detokenizer for neural text processing
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Evaluating neural network explanation methods using hybrid documents and morphosyntactic agreement
Nina Poerner, Hinrich Schütze, and Benjamin Roth. 2018 · 2018
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Seq2seq-vis: A visual debugging tool for sequence-to-sequence models
Hendrik Strobelt, Sebastian Gehrmann, Michael Behrisch, Adam Perer, Hanspeter Pfister, and Alexander M Rush. 2018 · 2018
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What can AI do for me? evaluating machine learning interpretations in cooperative play
Shi Feng and Jordan Boyd-Graber. 2019 · 2019
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Explanation in artificial intelligence: Insights from the social sciences
Tim Miller. 2019 · 2019
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Language models are unsupervised multitask learners
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Universal adversarial triggers for attacking and analyzing NLP
Eric Wallace, Shi Feng, Nikhil Kandpal, Matt Gardner, and Sameer Singh. 2019 · 2019
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Interpreting interpretability: Understanding data scientists’ use of interpretability tools for machine learning
Harmanpreet Kaur, Harsha Nori, Samuel Jenkins, Rich Caruana, Hanna Wallach, and Jennifer Wortman Vaughan. 2020 · 2020
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The language interpretability tool: Extensible, interactive visualizations and analysis for NLP models
Ian Tenney, James Wexler, Jasmijn Bastings, Tolga Bolukbasi, Andy Coenen, Sebastian Gehrmann, Ellen Jiang, Mahima Pushkarna, Carey Radebaugh, Emily Reif, and Ann Yuan. 2020 · 2020
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Visual search: How do we find what we are looking for?
Jeremy M Wolfe. 2020 · 2020
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Interpreting language models with contrastive explanations
Kayo Yin and Graham Neubig. 2022 · 2022
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Josh Achiam, Steven Adler, Sandhini Agarwal, Lama Ahmad, Ilge Akkaya, Florencia Leoni Aleman, Diogo Almeida, Janko Altenschmidt, Sam Altman, Shyamal Anadkat, et al. 2023 · 2023
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From discovery to adoption: Understanding the ml practitioners’ interpretability journey
Narges Ashtari, Ryan Mullins, Crystal Qian, James Wexler, Ian Tenney, and Mahima Pushkarna. 2023 · 2023
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Gemini: a family of highly capable multimodal models
Gemini Team, Rohan Anil, Sebastian Borgeaud, Yonghui Wu, Jean-Baptiste Alayrac, Jiahui Yu, Radu Soricut, Johan Schalkwyk, Andrew M Dai, Anja Hauth, et al. 2023 · 2023
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Ecco: An open source library for the explainability of transformer language models
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Training verifiers to solve math word problems
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Contrastive explanations for model interpretability
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LMdiff: A visual diff tool to compare language models
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Constitutional AI: Harmlessness from AI feedback
Yuntao Bai, Saurav Kadavath, Sandipan Kundu, Amanda Askell, Jackson Kernion, Andy Jones, Anna Chen, Anna Goldie, Azalia Mirhoseini, Cameron McKinnon, et al. 2022 · 2022
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“will you find these shortcuts?” a protocol for evaluating the faithfulness of input salience methods for text classification
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Promptmaker: Prompt-based prototyping with large language models
Ellen Jiang, Kristen Olson, Edwin Toh, Alejandra Molina, Aaron Donsbach, Michael Terry, and Carrie J Cai. 2022 · 2022
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Albert Q Jiang, Alexandre Sablayrolles, Arthur Mensch, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Florian Bressand, Gianna Lengyel, Guillaume Lample, Lucile Saulnier, et al. 2023 · 2023
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Faithfulness measurable masked language models
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Using captum to explain generative language models
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Inseq: An interpretability toolkit for sequence generation models
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Llama 2: Open foundation and fine-tuned chat models
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Longrope: Extending llm context window beyond 2 million tokens
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Towards Faithful Model Explanation in NLP: A Survey
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ConstitutionMaker: Interactively critiquing large language models by converting feedback into principles
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Toolformer: Language models can teach themselves to use tools
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